




'''simple docstring'''



import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def       _UpperCamelCase			()-> int:




     '''simple docstring'''





     __snake_case            =	'''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
     __snake_case            =	Image.open(requests.get(_lowerCamelCase ,						stream=_lowerCamelCase						).raw						).convert('''RGB'''						)

     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any]						)-> List[Any]:




     '''simple docstring'''





     __snake_case            =	[]
     # fmt: off

     # vision encoder
     rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''')						)
     rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''')						)
     rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''')						)
     rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''')						)
     rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''')						)
     rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''')						)

     for i in range(config.vision_config.num_hidden_layers						):
          rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight''')						)
          rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias''')						)
          rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight''')						)
          rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias''')						)
          rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')						)
          rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)						)
          rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')						)
          rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')						)
          rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')						)
          rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')						)
          rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')						)

     # QFormer
     rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.layernorm.weight''')						)
     rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''')						)

     # fmt: on
     return rename_keys







def       _UpperCamelCase			(_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  Optional[int]						)-> Tuple:




     '''simple docstring'''





     __snake_case            =	dct.pop(_lowerCamelCase						)
     __snake_case            =	val







def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Tuple						)-> str:




     '''simple docstring'''





     for i in range(config.vision_config.num_hidden_layers						):
          # read in original q and v biases
          __snake_case            =	state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias'''						)
          __snake_case            =	state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias'''						)

          # next, set bias in the state dict
          __snake_case            =	torch.cat((q_bias, torch.zeros_like(_lowerCamelCase ,						requires_grad=_lowerCamelCase						), v_bias)						)
          __snake_case            =	qkv_bias







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  Tuple						)-> Dict:




     '''simple docstring'''





     __snake_case            =	3_64 if '''coco''' in model_name else 2_24
     __snake_case            =	BlipaVisionConfig(image_size=_lowerCamelCase						).to_dict()

     # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
     # seems like flan-T5 models don't have bos_token_id properly set?
     if "opt-2.7b" in model_name:
          __snake_case            =	OPTConfig.from_pretrained('''facebook/opt-2.7b''' ,						eos_token_id=_lowerCamelCase						).to_dict()
     elif "opt-6.7b" in model_name:
          __snake_case            =	OPTConfig.from_pretrained('''facebook/opt-6.7b''' ,						eos_token_id=_lowerCamelCase						).to_dict()
     elif "t5-xl" in model_name:
          __snake_case            =	TaConfig.from_pretrained('''google/flan-t5-xl''' ,						dense_act_fn='''gelu''' ,						bos_token_id=1						).to_dict()
     elif "t5-xxl" in model_name:
          __snake_case            =	TaConfig.from_pretrained('''google/flan-t5-xxl''' ,						dense_act_fn='''gelu''' ,						bos_token_id=1						).to_dict()

     __snake_case            =	BlipaConfig(vision_config=_lowerCamelCase ,						text_config=_lowerCamelCase						)

     return config, image_size







@torch.no_grad()
def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Union[str, Any]=None ,						_lowerCamelCase				:  Any=False						)-> Dict:




     '''simple docstring'''





     __snake_case            =	(
         AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''						)
         if '''opt''' in model_name
         else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''						)
     )
     __snake_case            =	tokenizer('''\n''' ,						add_special_tokens=_lowerCamelCase						).input_ids[0]
     __snake_case       ,     __snake_case            =	get_blipa_config(_lowerCamelCase ,						eos_token_id=_lowerCamelCase						)

     __snake_case            =	BlipaForConditionalGeneration(_lowerCamelCase						).eval()

     __snake_case            =	{
         '''blip2-opt-2.7b''': ('''blip2_opt''', '''pretrain_opt2.7b'''),
         '''blip2-opt-6.7b''': ('''blip2_opt''', '''pretrain_opt6.7b'''),
         '''blip2-opt-2.7b-coco''': ('''blip2_opt''', '''caption_coco_opt2.7b'''),
         '''blip2-opt-6.7b-coco''': ('''blip2_opt''', '''caption_coco_opt6.7b'''),
         '''blip2-flan-t5-xl''': ('''blip2_t5''', '''pretrain_flant5xl'''),
         '''blip2-flan-t5-xl-coco''': ('''blip2_t5''', '''caption_coco_flant5xl'''),
         '''blip2-flan-t5-xxl''': ('''blip2_t5''', '''pretrain_flant5xxl'''),
     }

     __snake_case       ,     __snake_case            =	model_name_to_original[model_name]

     # load original model
     print('''Loading original model...'''						)
     __snake_case            =	'''cuda''' if torch.cuda.is_available() else '''cpu'''
     __snake_case       ,     __snake_case       ,     __snake_case            =	load_model_and_preprocess(
         name=_lowerCamelCase ,						model_type=_lowerCamelCase ,						is_eval=_lowerCamelCase ,						device=_lowerCamelCase						)
     original_model.eval()
     print('''Done!'''						)

     # update state dict keys
     __snake_case            =	original_model.state_dict()
     __snake_case            =	create_rename_keys(_lowerCamelCase						)
     for src, dest in rename_keys:
          rename_key(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)

     # some keys can be renamed efficiently
     for key, val in state_dict.copy().items():
          __snake_case            =	state_dict.pop(_lowerCamelCase						)
          if key.startswith('''Qformer.bert'''						):
               __snake_case            =	key.replace('''Qformer.bert''' ,						'''qformer'''						)
          if "attention.self" in key:
               __snake_case            =	key.replace('''self''' ,						'''attention'''						)
          if "opt_proj" in key:
               __snake_case            =	key.replace('''opt_proj''' ,						'''language_projection'''						)
          if "t5_proj" in key:
               __snake_case            =	key.replace('''t5_proj''' ,						'''language_projection'''						)
          if key.startswith('''opt'''						):
               __snake_case            =	key.replace('''opt''' ,						'''language'''						)
          if key.startswith('''t5'''						):
               __snake_case            =	key.replace('''t5''' ,						'''language'''						)
          __snake_case            =	val

     # read in qv biases
     read_in_q_v_bias(_lowerCamelCase ,						_lowerCamelCase						)

     __snake_case       ,     __snake_case            =	hf_model.load_state_dict(_lowerCamelCase ,						strict=_lowerCamelCase						)
     assert len(_lowerCamelCase						) == 0
     assert unexpected_keys == ["qformer.embeddings.position_ids"]

     __snake_case            =	load_demo_image()
     __snake_case            =	vis_processors['''eval'''](_lowerCamelCase						).unsqueeze(0						).to(_lowerCamelCase						)
     __snake_case            =	tokenizer(['''\n'''] ,						return_tensors='''pt'''						).input_ids.to(_lowerCamelCase						)

     # create processor
     __snake_case            =	BlipImageProcessor(
         size={'''height''': image_size, '''width''': image_size} ,						image_mean=_lowerCamelCase ,						image_std=_lowerCamelCase						)
     __snake_case            =	BlipaProcessor(image_processor=_lowerCamelCase ,						tokenizer=_lowerCamelCase						)
     __snake_case            =	processor(images=_lowerCamelCase ,						return_tensors='''pt'''						).pixel_values.to(_lowerCamelCase						)

     # make sure processor creates exact same pixel values
     assert torch.allclose(_lowerCamelCase ,						_lowerCamelCase						)

     original_model.to(_lowerCamelCase						)
     hf_model.to(_lowerCamelCase						)
     with torch.no_grad():
          if "opt" in model_name:
               __snake_case            =	original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}						).logits
               __snake_case            =	hf_model(_lowerCamelCase ,						_lowerCamelCase						).logits
          else:
               __snake_case            =	original_model(
                   {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}						).logits
               __snake_case            =	input_ids.masked_fill(input_ids == tokenizer.pad_token_id ,						-1_00						)
               __snake_case            =	hf_model(_lowerCamelCase ,						_lowerCamelCase ,						labels=_lowerCamelCase						).logits

     assert original_logits.shape == logits.shape
     print('''First values of original logits:''' ,						original_logits[0, :3, :3]						)
     print('''First values of HF logits:''' ,						logits[0, :3, :3]						)

     # assert values
     if model_name == "blip2-flan-t5-xl":
          __snake_case            =	torch.tensor(
              [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]] ,						device=_lowerCamelCase						)
          assert torch.allclose(logits[0, :3, :3] ,						_lowerCamelCase ,						atol=1E-4						)
     elif model_name == "blip2-flan-t5-xl-coco":
          __snake_case            =	torch.tensor(
              [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]] ,						device=_lowerCamelCase						)
     else:
          # cast to same type
          __snake_case            =	logits.dtype
          assert torch.allclose(original_logits.to(_lowerCamelCase						) ,						_lowerCamelCase ,						atol=1E-2						)
     print('''Looks ok!'''						)

     print('''Generating a caption...'''						)
     __snake_case            =	''''''
     __snake_case            =	tokenizer(_lowerCamelCase ,						return_tensors='''pt'''						).input_ids.to(_lowerCamelCase						)

     __snake_case            =	original_model.generate({'''image''': original_pixel_values}						)
     __snake_case            =	hf_model.generate(
         _lowerCamelCase ,						_lowerCamelCase ,						do_sample=_lowerCamelCase ,						num_beams=5 ,						max_length=30 ,						min_length=1 ,						top_p=0.9 ,						repetition_penalty=1.0 ,						length_penalty=1.0 ,						temperature=1 ,						)
     print('''Original generation:''' ,						_lowerCamelCase						)
     __snake_case            =	input_ids.shape[1]
     __snake_case            =	processor.batch_decode(outputs[:, prompt_length:] ,						skip_special_tokens=_lowerCamelCase						)
     __snake_case            =	[text.strip() for text in output_text]
     print('''HF generation:''' ,						_lowerCamelCase						)

     if pytorch_dump_folder_path is not None:
          processor.save_pretrained(_lowerCamelCase						)
          hf_model.save_pretrained(_lowerCamelCase						)

     if push_to_hub:
          processor.push_to_hub(f'''nielsr/{model_name}'''						)
          hf_model.push_to_hub(f'''nielsr/{model_name}'''						)


if __name__ == "__main__":
     UpperCAmelCase_							:	Any	     =		argparse.ArgumentParser()
     UpperCAmelCase_							:	Tuple	     =		[
         '''blip2-opt-2.7b''',
         '''blip2-opt-6.7b''',
         '''blip2-opt-2.7b-coco''',
         '''blip2-opt-6.7b-coco''',
         '''blip2-flan-t5-xl''',
         '''blip2-flan-t5-xl-coco''',
         '''blip2-flan-t5-xxl''',
     ]
     parser.add_argument(
         '''--model_name''',
         default='''blip2-opt-2.7b''',
         choices=choices,
         type=str,
         help='''Path to hf config.json of model to convert''',
     )
     parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
     parser.add_argument(
         '''--push_to_hub''',
         action='''store_true''',
         help='''Whether to push the model and processor to the hub after converting''',
     )

     UpperCAmelCase_							:	List[str]	     =		parser.parse_args()

     convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar







class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					Dict      		=					logging.get_logger()

														# the current default level is logging.WARNING
														A      :					List[Any]      		=					logging.get_verbosity()

														logging.set_verbosity_error()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														logging.set_verbosity_warning()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														logging.set_verbosity_info()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														logging.set_verbosity_debug()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														# restore to the original level
														logging.set_verbosity(__UpperCAmelCase						)




							def       snake_case			(					self						)		->       str:
														A      :					Any      		=					logging.get_verbosity()

														A      :					Optional[Any]      		=					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)
														A      :					Tuple      		=					'''Testing 1, 2, 3'''

														# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
														if level_origin <= logging.WARNING:
																					with CaptureLogger(__UpperCAmelCase						) as cl:
																												logger.warning(__UpperCAmelCase						)
																					self.assertEqual(cl.out    ,						msg + '''\n'''						)

														# this is setting the level for all of `transformers.*` loggers
														logging.set_verbosity_error()

														# should not be able to log warnings
														with CaptureLogger(__UpperCAmelCase						) as cl:
																					logger.warning(__UpperCAmelCase						)
														self.assertEqual(cl.out    ,						''''''						)

														# should be able to log warnings again
														logging.set_verbosity_warning()
														with CaptureLogger(__UpperCAmelCase						) as cl:
																					logger.warning(__UpperCAmelCase						)
														self.assertEqual(cl.out    ,						msg + '''\n'''						)

														# restore to the original level
														logging.set_verbosity(__UpperCAmelCase						)




							@mockenv(TRANSFORMERS_VERBOSITY='''error'''						)
							def       snake_case			(					self						)		->       Optional[int]:
														# reset for the env var to take effect, next time some logger call is made
														transformers.utils.logging._reset_library_root_logger()
														# this action activates the env var
														A      :					int      		=					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)

														A      :					Any      		=					os.getenv('''TRANSFORMERS_VERBOSITY'''    ,						__UpperCAmelCase						)
														A      :					List[str]      		=					logging.log_levels[env_level_str]

														A      :					Optional[int]      		=					logging.get_verbosity()
														self.assertEqual(
														    __UpperCAmelCase    ,						__UpperCAmelCase    ,						f'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'    ,						)

														# restore to the original level
														A      :					str      		=					''''''
														transformers.utils.logging._reset_library_root_logger()




							@mockenv(TRANSFORMERS_VERBOSITY='''super-error'''						)
							def       snake_case			(					self						)		->       Optional[int]:
														# reset for the env var to take effect, next time some logger call is made
														transformers.utils.logging._reset_library_root_logger()
														A      :					str      		=					logging.logging.getLogger()
														with CaptureLogger(__UpperCAmelCase						) as cl:
																					# this action activates the env var
																					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)
														self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''    ,						cl.out						)

														# no need to restore as nothing was changed






							def       snake_case			(					self						)		->       Optional[int]:
														# testing `logger.warning_advice()`
														transformers.utils.logging._reset_library_root_logger()

														A      :					Union[str, Any]      		=					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)
														A      :					Optional[int]      		=					'''Testing 1, 2, 3'''

														with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''						):
																					# nothing should be logged as env var disables this method
																					with CaptureLogger(__UpperCAmelCase						) as cl:
																												logger.warning_advice(__UpperCAmelCase						)
																					self.assertEqual(cl.out    ,						''''''						)

														with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''						):
																					# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
																					with CaptureLogger(__UpperCAmelCase						) as cl:
																												logger.warning_advice(__UpperCAmelCase						)
																					self.assertEqual(cl.out    ,						msg + '''\n'''						)






def        snake_case__		(    ):
							disable_progress_bar()
							assert are_progress_bars_disabled()

							enable_progress_bar()
							assert not are_progress_bars_disabled()






